A Comprehensive Survey on Artificial Intelligence Applications Across Interdisciplinary Domains: Techniques, Challenges, and Future Directions

Abstract

Artificial Intelligence (AI) has rapidly evolved into a transformative technology with far-reaching applications across healthcare, finance, cybersecurity, natural language processing, computer vision, and beyond. This survey systematically reviews 120 recent studies published between 2023 and 2026, offering a comprehensive analysis of AI-driven methodologies including deep learning, federated learning, reinforcement learning, differential privacy, and multimodal data fusion. The reviewed works are organized thematically and cited in sequential order to facilitate a coherent narrative of the current research landscape. In addition, we present comparative tables summarizing key techniques, application domains, and open challenges. Our analysis reveals that while AI has achieved significant breakthroughs in domain-specific applications, persistent challenges related to data privacy, model interpretability, algorithmic fairness, and cross-domain transferability remain. This survey aims to provide researchers and practitioners with a holistic overview and to inspire future interdisciplinary research endeavors.